Download 2nd Annual International Symposium on Information - CEUR

Overview of SIMBig 2015: 2nd Annual International
Symposium on Information Management and Big Data
Juan Antonio Lossio-Ventura
Hugo Alatrista-Salas
LIRMM, University of Montpellier Pontificia Universidad Católica del Perú
Montpellier, France
Lima, Peru
[email protected]
[email protected]
Abstract
international actors in the decision-making field to
state in new technologies dedicated to handle large
amount of information.
Our first edition, SIMBig 20144 took place in
Cuzco Peru too in September 2015. SIMBig 2014
has been indexed on DBLP5 (Lossio-Ventura and
Alatrista-Salas, 2014) and on CEUR Workshop
Proceedings6 .
Big Data is a popular term used to describe the exponential growth and availability of both structured and unstructured
data. The aim of the symposium is to
present the analysis methods for managing
large volumes of data through techniques
of artificial intelligence and data mining.
Bringing together main national and international actors in the decision-making field
to state in new technologies dedicated to
handle large amount of information.
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1.1
Keynote Speakers
SIMBig 2015 second edition has welcomed five
keynote speakers experts in Big Data, Data Mining, Natural Language Processing (NLP), and Social Networks:
Introduction
• PhD. Pr. Albert Bifet, from HUAWEI
Noah’s Ark Lab, China;
Big Data is a popular term used to describe the
exponential growth and availability of both structured and unstructured data. This has taken place
over the last 20 years. For instance, social networks such as Facebook, Twitter and Linkedin generate masses of data, which is available to be accessed by other applications. Several domains, including biomedicine, life sciences and scientific research, have been a↵ected by Big Data1 . Therefore
there is a need to understand and exploit this data.
This process can be carried out thanks to “Big
Data Analytics” methodologies, which are based
on Data Mining, Natural Language Processing,
etc. That allows us to gain new insight through
data-driven research (Madden, 2012; Embley and
Liddle, 2013). A major problem hampering Big
Data Analytics development is the need to process
several types of data, such as structured, numeric
and unstructured data (e.g. video, audio, text, image, etc)2 .
Therefore, the second edition of the Annual International Symposium on Information Management and Big Data - SIMBig 20153 , aims to present
the analysis methods for managing large volumes
of data through techniques of artificial intelligence
and data mining. Counting with main national and
• PhD. Pr. Diana Inkpen, from University of
Ottawa, Canada;
• PhD. Pr. Pascal Poncelet, from LIRMM
Laboratory and University of Montpellier,
France;
• PhD. Pr. Mathieu Roche, from Cirad and
TETIS Laboratory, France;
• PhD. Pr. Osmar R. Zaı̈ane, from University
of Alberta, Canada.
1.2
Scope and Topics
To share the new analysis methods for managing
large volumes of data, we encouraged participation
from researchers in all fields related to Big Data,
Data Mining, and Natural Language Processing,
but also Multilingual Text Processing, Biomedical
NLP. Topics of interest of SIMBig 2015 included
but were not limited to:
• Big Data
• Data Mining
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• Natural Language Processing
By 2015 the average of data annually generated in
hospitals is 665TB: http://ihealthtran.com/wordpress/2013/
03/infographic-friday-the-body-as-a-source-of-big-data/.
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Today, 80% of data is unstructured such as images,
video, and notes
3
http://simbig.org/SIMBig2015/
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https://www.lirmm.fr/simbig2014/
http://dblp2.uni-trier.de/db/conf/simbig/
simbig2014
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http://ceur-ws.org/Vol-1318/index.html
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• Bio NLP
• Promote the research in Peruvian universities,
mainly those belonging to the local organizing
committee.
• Text Mining
• Information Retrieval
• Create connections, forming networks of partnerships between companies and universities.
• Machine Learning
• Promote the local and international tourism,
in order to show to the participants the architecture, gastronomy and local cultural heritage.
• Semantic Web
• Ontologies
• Web Mining
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• Knowledge Representation and Linked Open
Data
Web and text intelligence are related areas that
have been used to improve human computer interaction both in general and in particular to explore and analyze information that is available on
the Internet. With the advent of social networks
and the emergence of services such as Facebook,
Twitter, and others, research in these areas has
been greatly enhanced. In recent years, shared
knowledge and experiences have established new
and di↵erent types of personal and communal relationships which have been leveraged by social networks scientists to produce new insights. In addition there has been a huge increase in community
activities on social networks.
The Web and Text Intelligence (WTI) track of
SIMBig 2015 have provided a forum that brought
together researchers and practitioners for exploring technologies, issues, experiences and applications that help us to understand the Web and to
build automatic tools to better exploit this complex environment. The WTI track has fostered
collaborations, exchange of ideas and experiences
among people working in a variety of highly crossdisciplinary research fields such as computer science, linguistics, statistics, sociology, economics,
and business.
The WTI track is a follow up of the 4th International Workshop on Web and Text Intelligence7 ,
which took place in Curitiba, Brazil, October 2012,
as a workshop of BRACIS 2012; the 3rd International Workshop on Web and Text Intelligence8 ,
which took place in São Bernardo, Brazil, October
2010, as a workshop of SBIA10; the 2nd International Workshop on Web and Text Intelligence9 ,
which took place in São Carlos, Brazil, September
2009, as a workshop of STIL09; the 1st Web and
Network Intelligence10 , which took place in Aveiro,
Portugal, October 2009, as a thematic track of
EPIA09; and the 1st International Workshop on
Web and Text Intelligence, which took place in
• Social Networks, Social Web, and Web Science
• Information visualization
• OLAP, Data Warehousing
• Business Intelligence
• Spatiotemporal Data
• Health Care
• Agent-based Systems
• Reasoning and Logic
• Constraints, Satisfiability, and Search
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Track on Web and Text
Intelligence (WTI 2015)
Latin American and Peruvian
Academic Goals of the
Symposium
The academic goals of the symposium are varied,
among which we can list the following:
• Meet Latin American and foreign researchers,
teachers, and students belonging to several domains of computer sciences, specially related
to Big Data.
• Promote the production of scientific articles,
which will be evaluated by the international
scientific community, in order to receive a
feedback from experts.
• Foster partnerships between Latin American
universities, local universities and European
universities.
• Promote the creation of alliances between Peruvian universities, enabling decentralization
of education.
• Motivate students to learn more about computer sciences research to solve problems related to the management of information and
Big Data.
7
http://www.labic.icmc.usp.br/wti2012/
http://www.labic.icmc.usp.br/wti2010/
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http://www.labic.icmc.usp.br/wti2009/
10
http://epia2009.web.ua.pt/wni/
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Salvador, Brazil, October 2008, as a workshop of
SBIA08.
3.1
4.2
• iMedia14
• Bioincuba15
Scope and Topics
The topics of WTI include, but are not limited to:
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• TechnoPark16
• Grupo de Reconocimiento de Patrones e Inteligencia Artificial Aplicada, PUCP, Perú17
Web and Text Mining
Link Mining
Web usability
Web automation and adaptation
Graph and complex network mining
Communities analysis in social networks
Relationships analysis in social networks
Applications of social networks and social media
Data modeling for social networks and social
media
Location-based social networks analysis
Big data issues in social network and media
analysis
Modeling of user behavior and interactions
Temporal analysis of social networks and social media
Pattern analysis in social networks and social
media
Privacy and security in social networks
Propagation and di↵usion of information in
social network
Social information applied to recommender
systems
Search and Web Mining
Multimedia Web Mining
Visualization of social information
• Universidad Nacional Mayor de San Marcos,
Perú18
• Escuela de Post-grado de la Pontificia Universidad Católica del Perú19
4.3
WTI Organizing Institutions
• Instituto de Ciências Matemáticas e de Computação, USP, Brasil20
• Instituto Politécnico de Tomar, Portugal21
• Labóratorio de Intêligencia Computacional,
ICMC, USP, Brasil22
• Labóratorio de Intêligencia Artificial e Apoio
à Decisão, INESC TEC, Portugal23
• Machine Learning Lab (MaLL), UFSCar,
Brasil24
• Universidade Federal de São Carlos, Brasil25
References
David W Embley and Stephen W Liddle. 2013.
Big data—conceptual modeling to the rescue. In
Conceptual Modeling, ER’13, pages 1–8. LNCS,
Springer.
Juan Antonio Lossio-Ventura and Hugo AlatristaSalas, editors. 2014. Proceedings of the 1st Symposium on Information Management and Big
Data - SIMBig 2014, Cusco, Peru, September 810, 2014, volume 1318 of CEUR Workshop Proceedings. CEUR-WS.org.
Sponsors
We want to thank our wonderful sponsors! We
extend our sincere appreciation to our sponsors,
without whom our symposium would not be possible. They showed their commitment to making
our research communities more active. We invite
you to support these community-minded organizations.
Sam Madden. 2012. From databases to big data.
volume 16, pages 4–6. IEEE Educational Activities Department, Piscataway, NJ, USA, may.
14
http://www.imedia.pe/
http://www.bioincuba.com/
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http://technoparkidi.org/
17
http://inform.pucp.edu.pe/~grpiaa/
18
http://www.unmsm.edu.pe/
19
http://posgrado.pucp.edu.pe/la-escuela/
presentacion/
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http://www.icmc.usp.br/Portal/
21
http://portal2.ipt.pt/
22
http://labic.icmc.usp.br/
23
http://www.inesctec.pt/liaad
24
http://ppgcc.dc.ufscar.br/pesquisa/
laboratorios-e-grupos-de-pesquisa
25
http://www2.ufscar.br/home/index.php
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4.1
Collaborating Institutions
Organizing Institutions
• Université de Montpellier, France11
• Laboratoire de Informatique, Robotique et
Microélectronique de Montpellier, France12
• Universidad Andina del Cusco, Perú13
11
http://www.umontpellier.fr/
http://www.lirmm.fr/
13
http://www.uandina.edu.pe/
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